Simulation research for mobile robot path planning based on improved artificial potential field method recommended by the AsiaSim

2017 ◽  
Vol 08 (02) ◽  
pp. 1750046 ◽  
Author(s):  
Pengqi Hou ◽  
Hu Pan ◽  
Chen Guo
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Potential Field ◽  
Field Method ◽  
Repulsive Force ◽  
Artificial Potential Field ◽  
Target Point ◽  
Robot Path Planning ◽  
Simulation Research ◽  
Robot Path

Mobile robot path planning is an important research branch in the field of mobile robots. The main disadvantage of the traditional artificial potential field (APF) method is prone to local minima problems. Improved artificial potential field (IAPF) method is presented in this paper to solve the problem in the traditional APF method for robot path planning in different conditions. We introduce the distance between the robot and the target point to the function of the original repulsive force field and change the original direction of the repulsive force to avoid the trap problem caused by the local minimum point. The IAPF method is suitable for mobile robot path planning in the complicated environment. Simulation and experiment results at the robot platform illustrated the superiority of the modified IAPF method.

scholarly journals An Improved Path Planning Method Based on Artificial Potential Field for a Mobile Robot

2015 ◽  
Vol 15 (2) ◽  
pp. 181-191 ◽  
Author(s):  
Wenbai Chen ◽  
Xibao Wu ◽  
Yang Lu
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Potential Field ◽  
Field Method ◽  
Artificial Potential Field ◽  
Improved Method ◽  
Chaos Optimization ◽  
Potential Field Method ◽  
Artificial Potential Field Method ◽  
Robot Path

Abstract To solve the problem of local minima and unreachable destination of the traditional artificial potential field method in mobile robot path planning, chaos optimization is introduced to improve the artificial potential field method. The potential field function was adopted as a target function of chaos optimization, and a kind of “two-stage” chaos optimization was used. The corresponding movement step and direction of the robot were achieved by chaos search. Comparison of the improved method proposed in this paper and the traditional artificial potential field method is performed by simulation. The simulation results show that the improved method gets rid of the drawbacks, such as local minima and unreachable goal. Furthermore, the improved method is also verified by building up a physical platform based on “Future Star” robot. The success of the physical experiment indicates that the improved algorithm is feasible and efficient for mobile robot path planning.

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